Elynn Chen

Elynn Chen is an Assistant Professor in the Department of Technology, Operations, and Statistics (TOPS) at the Leonard N. Stern School of Business, New York University, where she has been a faculty member since September 2021. Her research bridges statistics, machine learning, and operations research with applications in business, economics, and healthcare.

Her educational background is highly interdisciplinary:

• Ph.D. in Statistics, Rutgers University
• B.A. in Economics, Peking University
• B.S. in Computer Science, Tsinghua University

Professor Chen's research is centered on developing novel methodologies for data-driven decision-making and complex data analysis. Her primary interests include:

• Tensor learning for multi-dimensional data representation
• Reinforcement learning with applications in societal domains such as healthcare and education
• Transfer learning and knowledge fusion across heterogeneous tasks
• High-dimensional time series and matrix-variate factor models

She emphasizes algorithmic innovation and statistical rigor in addressing real-world challenges.

Her recent publications reveal a consistent focus on advanced statistical learning methods. She has made significant contributions to tensor decomposition, reinforcement learning in heterogeneous environments, and high-dimensional network modeling. Her work combines theoretical depth with practical applications in international trade, clinical treatments, and corporate finance.

Her scientific recognition includes:

• NSF Postdoctoral Research Award DMS-1803241

She actively mentors students and postdoctoral researchers, fostering a collaborative research environment. Her group works on cutting-edge topics such as tensor-view graph neural networks and dynamic matrix factor models. She has received research support through prestigious postdoctoral appointments at UC Berkeley (advised by Prof. Michael I. Jordan), Princeton University (with Prof. Jianqing Fan), and OpenAI.

She leads a vibrant research team focused on:

• Tensor learning
• Reinforcement learning for social applications
• Transfer and knowledge fusion

She welcomes highly motivated individuals to join her research group.